Disentangled representation learning plays a pivotal role in making representations controllable, interpretable and transferable. Despite its significance in the domain, the quest for reliable and consistent quantitative disentanglement metric remains a major challenge. This stems from the utilisation of diverse metrics measuring different properties and the potential bias introduced by their design. Our work undertakes a comprehensive examination of existing popular disentanglement evaluation metrics, comparing them in terms of measuring aspects of disentanglement (viz. Modularity, Compactness, and Explicitness), detecting the factor-code relationship, and describing the degree of disentanglement. We propose a new framework for quantifying disentanglement, introducing a metric entitled \emph{EDI}, that leverages the intuitive concept of \emph{exclusivity} and improved factor-code relationship to minimize ad-hoc decisions. An in-depth analysis reveals that EDI measures essential properties while offering more stability than existing metrics, advocating for its adoption as a standardised approach.

本研究解决了在分离表示学习领域中缺乏可靠且一致的量化评估度量的问题。通过全面分析现有度量，提出了一种新框架和度量指标EDI，该指标利用“独占性”这一直观概念，引入了改进的因子-编码关系，展现出比现有度量更优的稳定性和重要特性，建议将其作为标准化方法使用。

改进分离表示评估度量的方法